Dynamically adaptive framework and method for classifying malware using intelligent static, emulation, and dynamic analyses

ABSTRACT

Techniques for malware detection are described herein. According to one aspect, control logic determines an analysis plan for analyzing whether a specimen should be classified as malware, where the analysis plan identifies at least first and second analyses to be performed. Each of the first and second analyses identified in the analysis plan including one or both of a static analysis and a dynamic analysis. The first analysis is performed based on the analysis plan to identify suspicious indicators characteristics related to processing of the specimen. The second analysis is performed based on the analysis plan to identify unexpected behaviors having processing or communications anomalies. A classifier determines whether the specimen should be classified as malicious based on the static and dynamic analyses. The analysis plan, the indicators, the characteristics, and the anomalies are stored in a persistent memory.

FIELD OF THE INVENTION

Embodiments of the present invention relate generally to malicious content detection. More particularly, embodiments of the invention relate to malicious content detection using intelligent static and dynamic analyses.

BACKGROUND

Malicious software, or malware for short, may include any program or file that is harmful by design to a computer. Malware includes computer viruses, worms, Trojan horses, adware, spyware, and any programming that gathers information about a computer or its user or otherwise operates without permission. The owners of the computers are often unaware that these programs have been added to their computers and are often similarly unaware of their function.

Malicious network content is a type of malware distributed over a network via websites, e.g., servers operating on a network according to a hypertext transfer protocol (HTTP) standard or other well-known standard. Malicious network content distributed in this manner may be actively downloaded and installed on a computer, without the approval or knowledge of its user, simply by the computer accessing the web site hosting the malicious network content (the “malicious web site”). Malicious network content may be embedded within objects associated with web pages hosted by the malicious web site. Malicious network content may also enter a computer upon receipt or opening of email. For example, email may contain an attachment, such as a PDF document, with embedded malicious executable programs. Furthermore, malicious content may exist in files contained in a computer memory or storage device, having infected those files through any of a variety of attack vectors.

Various processes and devices have been employed to prevent the problems associated with malicious content. For example, computers often run antivirus scanning software that scans a particular computer for viruses and other forms of malware. The scanning typically involves automatic detection of a match between content stored on the computer (or attached media) and a library or database of signatures of known malware. The scanning may be initiated manually or based on a schedule specified by a user or system administrator associated with the particular computer. Unfortunately, by the time malware is detected by the scanning software, some damage on the computer or loss of privacy may have already occurred, and the malware may have propagated from the infected computer to other computers. Additionally, it may take days or weeks for new signatures to be manually created, the scanning signature library updated and received for use by the scanning software, and the new signatures employed in new scans.

Moreover, anti-virus scanning utilities may have limited effectiveness to protect against all exploits by polymorphic malware. Polymorphic malware has the capability to mutate to defeat the signature match process while keeping its original malicious capabilities intact. Signatures generated to identify one form of a polymorphic virus may not match against a mutated form. Thus polymorphic malware is often referred to as a family of virus rather than a single virus, and improved anti-virus techniques to identify such malware families is desirable.

Another type of malware detection solution employs virtual environments to replay content within a sandbox established by virtual machines (VMs). Such solutions monitor the behavior of content during execution to detect anomalies that may signal the presence of malware. One such system offered by FireEye®, Inc., the assignee of the present patent application, employs a two-phase malware detection approach to detect malware contained in network traffic monitored in real-time. In a first or “static” phase, a heuristic is applied to network traffic to identify and filter packets that appear suspicious in that they exhibit characteristics associated with malware. In a second or “dynamic” phase, the suspicious packets (and typically only the suspicious packets) are replayed within one or more virtual machines. For example, if a user is trying to download a file over a network, the file is extracted from the network traffic and analyzed in the virtual machine. The results of the analysis aids in determining whether the file is malicious. The two-phase malware detection solution may detect numerous types of malware and, even malware missed by other commercially available approaches. Through verification, the two-phase malware detection solution may also achieve a significant reduction of false positives relative to such other commercially available approaches. Dealing with false positives in malware detection may needlessly slow or interfere with download of network content or receipt of email, for example. This two-phase approach has even proven successful against many types of polymorphic malware and other forms of advanced persistent threats.

Typically, the static phase and the dynamic phase are performed in sequence, in which a static analysis is performed followed by a dynamic analysis, to generate separate scores with limited or no influence from each other. The scores are then used to determine the final malware score of the content for content classification. The static or dynamic phase may be performed in an operating environment that may not be correct and/or necessary. For example, a dynamic analysis may be performed on the content for specific types and/or versions of operating systems and/or applications executing within a virtual environment, even if a static analysis reveals that the content is intended for a particular version of a particular type of operating system and/or application. As a result, drawbacks of known two-phase malware detection solutions include a certain inflexibility and inefficiency in performing the analysis.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the invention are illustrated by way of example and not limitation in the figures of the accompanying drawings in which like references indicate similar elements.

FIGS. 1A and 1B are block diagrams illustrating a malware detection system according to certain embodiments of the invention.

FIG. 2 is a block diagram illustrating an example of a controller according to one embodiment of the invention.

FIG. 3 is a block diagram illustrating a static analysis module according to one embodiment of the invention.

FIG. 4 is a block diagram illustrating a malware classifier according to one embodiment of the invention.

FIG. 5 is a flow diagram illustrating a method for malware detection according to one embodiment of the invention.

FIGS. 6A and 6B are flow diagrams illustrating a method for malware detection according to some embodiments of the invention.

FIG. 7 is a block diagram illustrating a possible implementation of a malware detection system according to one embodiment of the invention.

FIG. 8 is a block diagram of a computer network system deploying a malicious content detection system according to one embodiment of the invention.

DETAILED DESCRIPTION

Various embodiments and aspects of the invention will be described with reference to details discussed below, and the accompanying drawings will illustrate the various embodiments. The following description and drawings are illustrative of the invention and are not to be construed as limiting the invention. Numerous specific details are described to provide a thorough understanding of various embodiments of the present invention. However, in certain instances, well-known or conventional details are not described in order to provide a concise discussion of embodiments of the present inventions.

Reference in the specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in conjunction with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase “in one embodiment” in various places in the specification do not necessarily all refer to the same embodiment.

Techniques for malware detection using intelligent static analysis and dynamic analysis are described herein. According to one embodiment, a malware detection system includes, but is not limited to, a first analysis module (e.g., a static analysis module), a second analysis module (e.g., a dynamic analysis module), a malware classifier, and a controller. In response to receiving a specimen (e.g., a content item such as a data object or file to be analyzed) for malware detection, the controller determines an analysis plan for analyzing whether the specimen should be classified as malware. The analysis plan identifies at least a first analysis and possibly plural analyzes, along with an order and, in some embodiments, an analysis “protocol” for performing the analysis. For example, the analysis plan may specify a first analysis and a second analysis to be performed, which may be a static analysis and/or a dynamic analysis, and the order in which they are to be performed. In some embodiments, the analysis plan may also specify an analysis protocol and parameters, such as a specific operating environment to be provided or specific behaviors to be monitored in the dynamic analysis, specific types of static analyses, or specific characteristics to be checked, verified or examined via static analysis.

Where the analysis plan specifies two analyses, e.g., a first and second analysis, the first analysis may be performed by the first analysis module according to the analysis plan, for example, to identify one or more suspicious indicators and one or more characteristics related to processing of the specimen. A second analysis may be performed by the second analysis module in accordance with the analysis plan on the specimen, for example, to identify one or more unexpected behaviors that include one or more processing or communications anomalies. The results of the first and second analyses are provided to the classifier. The classifier is to classify the specimen based on the identified suspicious indicators and the anomalies. The analysis plan and all the information generated from the first and second analysis and the classification are stored in a persistent storage (which may be located locally and/or remotely), including the suspicious indicators, characteristics, information describing the unexpected and/or expected behaviors, as well as the specimen itself and the metadata describing the circumstances surrounding the specimen (e.g., email or Web information through which the specimen was received).

The controller uses the stored information to determine what, if any, additional analysis or analyses should be performed, and, often, what protocols should be followed during the subsequent testing. The sequence order of the analyses involved may be determined by the controller as part of the analysis plan, or update to the analysis plan. In one embodiment, the controller monitors or receives feedback from the analysis modules and the classifier, and may modify or adjust the analysis plan based on the results of a prior analysis and classification, including configuring an additional analysis between or after the first and second analysis or modifying a procedure or operating environment of the next analysis in the analysis plan.

Accordingly, the first analysis may be performed prior to the second analysis, where the second analysis may be performed based in part, for example, on the information or results generated from the first analysis, such as suspicious indicators and characteristics. Alternatively, the second analysis may be performed prior to the first analysis, where the first analysis may be performed based in part, for example, on at least one of the anomalies identified during the second analysis. Some embodiments of the invention may improve over the known two-phase malware detection solutions by providing a third type of analysis involving emulation as a simpler, more time efficient method of analysis than the dynamic analysis involving a virtual machine, either in lieu of or in addition to dynamic analysis. Such three-phase malware detection solutions provide additional options for the controller in conducting analysis.

As a result, embodiments of the invention may perform malware detection with greater flexibility in the conduct of the analysis, and realize greater efficiencies with improved efficacy in detecting malware than in known two-phase malware detection solutions.

With reference now to the drawings, FIG. 1A is a block diagram illustrating a malware detection system according to one embodiment of the invention. Referring to FIG. 1A, system 100 includes malicious content detection system 101 configured to receive a specimen or specimens 180 from a source (not shown) and to determine whether the specimen 180 should be classified as malicious. The term of specimen represents one or more data objects or a file (e.g., an executable, a document, a library, a media file), which may be suspicious or unknown. The specimen may be network content transmitted by a network node over a network (e.g., a local area network, a wide area network or the Internet, or a combination thereof) and captured by a network device such as a network tap. Alternatively, the specimen may be manually submitted by a user via a user interface (e.g., a Web portal). In one embodiment, malicious content detection system 101 includes, but is not limited to, static analysis module 102 (also referred to as static analysis logic or static analyzer), dynamic analysis module 103 (also referred to as dynamic analysis logic or dynamic analyzer), malware classifier 105, controller 106 (also referred to as control logic), and intelligence store or database 110.

Static analysis module 102 is to perform a static analysis on the specimen 180 without executing or playing the specimen. A static analysis may include signature match, protocol semantics anomalies check, source reputation check, malware source blacklist or whitelist checking, and/or emulation. Dynamic analysis module 103 is to perform a dynamic analysis on the specimen, including monitoring behaviors of the specimen 180 during its virtual execution to detect any unexpected behaviors having one or more anomalies. Malware classifier 105 is to classify whether the specimen is likely malicious based on the results of the static analysis and dynamic analysis, and other information such as information stored in the intelligence store 110. Controller 106 is to coordinate the operations of the static analysis module 102, the dynamic analysis module 103, and the classifier 105, including controlling the processing flows amongst them via an analysis plan or a feedback received from any of the static and dynamic analysis modules 102-103 and classifier 105. The controller 106 is to determine in an analysis plan whether one or both of the analysis modules 102-103 should be involved, the order of the analysis modules 103-103 involved (which may be in series or in parallel), whether additional analysis is needed based on the feedback from the classifier 105 and the intelligence information stored in the intelligence store 110. Effectively, controller 106 determines an analysis plan or roadmap for static analysis module 102, dynamic analysis module 103, and malware classifier 105. Although two analysis modules are shown in FIG. 1A, more or fewer analysis modules or other components may also be implemented.

According to one embodiment, the information stored in the intelligence store 110 (e.g., a persistent database) is accessible and used by each of the components of the malware detection system (e.g., static analysis module 102, dynamic analysis module 103, malware classifier 105, and controller 106) during all processing stages of malware detection processes. Each of the components may utilize the information stored in the intelligence store 110 during their respective processes. Each of the components may generate and store further intelligence information in the intelligence store 110 during their respective processes, which may be utilized in subsequent operations. The intelligence information stored in the intelligence store 110 includes a variety of information obtained during the current malware detection session and prior malware detection sessions (if any), and/or other information received or updated from other information sources, such as external analysis data and control server 120 in the cloud (e.g., over the Internet). The intelligence information may include metadata of the specimen, information concerning the circumstances surrounding the specimen (e.g., environment in which the specimen is received such as email or Web information), information observed or learned during the operations of each of the components of the malware detection system, and/or other information obtained from other malware detection systems with respect to the same or similar specimen. The specimen itself may also be cached in the intelligence store 110.

At least some of the components such as controller 106 may be equipped with a logger to log all the events or activities occurred during the processes of the respective components. The logged information may also be stored in intelligence store 110 and accessible by all components. As a result, each of the components of the malware detection system has all the intelligence information available from the intelligence store 110 during the corresponding stage of processes and it can perform a more comprehensive and accurate analysis in view of all the intelligence information generated from the past and current malware detection sessions. Since all components share all of the intelligence information, they effectively are on the “same page,” and communicate with one another (e.g., feedback), which enables each component to make intelligent decisions to improve the efficiency and accuracy of the malware detection. The information stored in intelligence store 110 may be stored in a persistent storage device (e.g., hard drive or flash memory device) and loaded in the system memory during the malware detection. The information stored in intelligence store 110 may be synchronized from time to time with a central management server such as server 120 for further analysis (e.g., offline analysis) and for sharing of information with other malicious content detection systems. For example, controller 106 may determine that the specimen has characteristics, identifiers, or behaviors that merit sending the specimen outside of the customer's network or sub-network (e.g., to a remote or centralized location, which may provide cloud-based subscription services) for additional (e.g., factory) processing, should the customer opt-in to this option.

In response to receiving a specimen for malware detection, the controller 106 determines an analysis plan for analyzing whether the specimen should be classified as malware. The specimen may be recently captured or received from a remote source or alternatively, it can be the same specimen that has been processed during a previous iteration of malware detection processes. Controller 106 determines a next analysis based on the received specimen and the results of a prior analysis. Controller 106 records this analysis decision in the analysis plan and the results of all analysis are stored in the memory in association with a specimen identifier identifying the received specimen. A specimen identifier may be a filename or other identifying information. The analysis plan identifies at least one analysis to be performed, for example, for purposes of the following discussion, a first and second analysis, each of which may be a static analysis and/or a dynamic analysis. A first analysis (e.g., static analysis) is then performed by the first analysis module (e.g., static analysis module 102) according to the analysis plan to identify one or more suspicious indicators and one or more characteristics related to processing of the specimen. In addition, certain non-suspicious indicators (e.g., predefined data patterns) may also be tracked. A second analysis (e.g., dynamic analysis) is performed by the second analysis module (e.g., dynamic analysis module 103) in accordance with the analysis plan on the specimen to identify one or more unexpected behaviors that include one or more processing or communications anomalies. Similarly, certain expected behaviors may also be recorded. The classifier 105 is to classify the specimen based on the identified suspicious indicators and the anomalies. The analysis plan and all the information generated from the first and second analysis and the classification are stored in a persistent storage, such as intelligence store 110 or external server 120.

The first analysis may be performed prior to the second analysis, where the second analysis may be performed based in part on the information or results generated from the first analysis, such as suspicious indicators and characteristics. Alternatively, the second analysis may be performed prior to the first analysis, where the first analysis may be performed based in part on at least one of the anomalies identified during the second analysis. Furthermore, controller 106 may perform an initial analysis or scanning on the received specimen and may decide to dispatch the specimen for both analysis modules 102-103 for static and dynamic analyses in parallel. In one embodiment, the controller 106 monitors or receives feedback from at least one of the analysis modules 102-103 and the classifier 105. Controller 106 may modify or adjust the analysis plan based on the results of the analysis and the classification, including configuring and initiating an additional analysis (e.g., static or dynamic analysis) between or after the first and second analysis or modifying a procedure (e.g., protocol) or environment settings of the next analysis in the analysis plan. Controller 106 may further specify the order of multiple analyses listed in the analysis plan. The analysis plan may be updated and maintained in the intelligence store 110.

In one embodiment, after performing a static analysis before performing a dynamic analysis, controller 106 may alter the analysis plan based on the result of the static analysis, as well as other information obtained from the intelligence store 110. Controller 106 may decide to perform an additional analysis, e.g., by adding processing of the specimen in an emulation analysis module to unpack an object and then another static analysis on the unpacked object. The dynamic analysis is then performed pursuant to the analysis plan based in part on the results of the inserted static analysis. Thus, a result of one analysis or operation may provide an influence to a subsequent analysis or operation. The influence may be any information or data that affects or alters the decision making regarding a subsequent analysis or the conduct or operation of the malware detection process during that subsequent analysis. For example, the influence generated by a static analysis on a subsequent dynamic analysis may include the runtime environment used by the subsequent dynamic analysis, including a type of operating system and its version, type of specimen (e.g., executable, PDF, Web, WORD), applications involved (e.g., browser), etc., the length of time to conduct a dynamic analysis on the specimen, or the type of behaviors to be monitored, or the type or location of monitors (or monitor instrumentation) to deploy. These are examples of the analysis protocol and parameters referred to above.

According to one embodiment, controller 106 may modify the priorities of the specimens to be analyzed in the analysis plan based on the information observed (from the intelligence store) at the point in time. Initially, for example, when the specimens are received for malware detection, controller 106 may perform an initial analysis on the specimens, associate a priority with each of the specimens, and set an order of the analyses to be performed in an analysis plan. After a first analysis (e.g., static analysis), controller 106 may modify the priorities of the specimens and/or the order of the analyses in the analysis plan based on the result of the first analysis. Controller 106 may further configure the time or analysis schedule for each of the analyses to be performed in the analysis plan. The time or analysis schedule information may also be stored in the analysis plan. Controller 106 then dispatches the specimens to be analyzed according to the analysis schedule or time specified in the analysis plan.

According to another embodiment, after a static analysis has been performed, based on the result of the static analysis, controller 106 may select a runtime environment of a dynamic analysis that is supposed to be performed after the static analysis. For example, controller 106 may determine an operating system and version thereof, an application and version thereof for the virtual environment of the dynamic analysis. Controller 106 may further select an initial state from which the application will be run based on the result of the static analysis. Controller 106 may alter the analysis plan to reflect such changes.

According to another embodiment, any results (e.g., events), activities, and/or decision makings of all of the components may be recorded (for example, by controller 106, or by the individual components themselves) in the analysis plan or an analysis log, which may be stored in database 110 and/or external storage 120. The recorded information may be stored in database 110, which may be indexed based on identifiers of the specimen. Controller 106 may determine a next analysis based on prior analysis results and dispatch the specimen to one or both of analysis modules 102-103 via a virtual switch, where the virtual switch is operated based on certain events or conditions maintained by the intelligence store 110. Controller 106 may also determine the length of a dynamic analysis and specific software to run therein, including an operating system, applications, libraries, plugins, and versions thereof based on the specimen or one or more prior analyses. Controller 106 may continue directing a further analysis or terminate the current analysis after a period of time, which may be determined based on a number of pending specimens.

In one embodiment, a static analysis may be performed in view of the intelligence information stored in intelligence store 110. A static analysis may include signature match, protocol semantics anomalies check, source reputation check and/or emulation. Static analysis module 102 further extracts information from the specimen that describes the specimen. The extracted information is stored in intelligence store 110. Static analysis module 102 may further generate intelligence information during the static analysis and store the intelligence information in intelligence store 110. Static analysis result 111 may also be stored in intelligence store 110. Static analysis module 102 may further perform an analysis based on a set of heuristics and to generate a static score representing the likelihood that a specimen is malicious based on the static analysis. The static score may be a measure of probability of malware and used in part by malware classifier 105 to classify the specimen.

In one embodiment, the specimen is statically inspected by static analysis module 102 for various attributes and “features.” These features are intended to be signals to both goodness and badness of the specimen. For example if a file contains a Microsoft® WORD® icon as its own display icon, this may “look” suspicious since that is a common malware technique to trick a user into opening the file. During the subsequent dynamic analysis, the file is dynamically analyzed by dynamic analysis module 103 for various behavioral actions, and it may be discovered that the file may not be opened by Microsoft WORD and/or may perform activities (e.g., behaviors) that are not expected of a WORD document. The “intelligent” aspect of the dynamic analysis is that the information from the static analysis can be used to help or influence the dynamic analysis. Such information may be stored in intelligence store 110.

Dynamic analysis module 103 is configured to monitor the behaviors of the specimen in an operating environment (e.g., virtual machine), generating a dynamic analysis result 112. Dynamic analysis result 112 may include information describing or indicating the unexpected and/or expected behaviors observed during the dynamic analysis. Dynamic analysis result 112 may be stored in the intelligence store 110 as well. The dynamic analysis may be configured and performed in view of the intelligence information obtained from the intelligence store 110. Dynamic analysis module 103 may further generate and store further intelligence information in intelligence store 110 during the dynamic analysis. Dynamic analysis module 103 may further generate a dynamic score representing the likelihood that specimen is malicious based on the dynamic analysis, which may be in a form of a measure of probability. Static analysis result 111 and dynamic analysis 112 are used by malware classifier 105 to determine, in view of the intelligence information obtained from the intelligence store 110, a malware classification indicator 109 that indicates whether the specimen is malicious, non-malicious, or uncertain, which may also be stored in the intelligence store 110. Malware classification indicator 109 may be in a form of confidence score.

Malware classification indicator 109 is fed back to controller 106 to determine whether the malware classification indicator 109 is sufficient or conclusive enough to classify the specimen. If so, controller 106 may terminate the analysis and reporting module 108 is invoked to report whether the specimen is indeed malware or non-malware. In the event the specimen is malware, a malware signature or malware identifier may also be generated for future detection. In the event the malware classification indicator 109 indicates the specimen is uncertain, controller 106 may configure additional analysis to be performed. Controller may further determine certain parameters or environment settings for the additional analysis based on the intelligence information obtained from the intelligence store 110. Controller 106 may further extend the clock time based on the results being obtained in the dynamic analysis or launch another dynamic analysis in response to those results.

According to one embodiment, the static analysis and dynamic analysis performed by static analysis module 102 and dynamic analysis module 103 may be performed in sequence (configured via an analysis plan) in which an analysis result of an analysis (e.g., static analysis) may be utilized, for example, via intelligence store 110, by a subsequent analysis (e.g., dynamic analysis) to improve the efficiency and accuracy of the subsequent analysis. In one embodiment, when a specimen is received, for example, via a network tap, for malware detection, controller 106 determines which of the static analysis and dynamic analysis should be performed first. For certain types of content (e.g., portable document format (PDF), a dynamic-linked library (DLL)), a static analysis may be performed first and a dynamic analysis may then be performed. For other types of content (e.g., Web page or an executable), a dynamic analysis may be performed prior to a static analysis.

According to one embodiment, an analysis module generates further intelligent information concerning the content in question, such as a type of content, and/or an operating system and its version in which the content is intended to be executed. Such intelligent information is utilized by another analysis module to perform a subsequent analysis in a manner specifically tailored to the content in question. For example, the result of a static analysis can be used to configure an operating environment that is specifically tailored to the content for the dynamic analysis.

According to one embodiment, if controller 106 determines that there is a discrepancy between intelligent information provided by static analysis module 102 and dynamic analysis module 103 (which may be stored in intelligence store 110 or received via an application programming interface or API), it may configure additional analysis to be performed. For example, a first static analysis may reveal a first set of features of a specimen in question. However, after a first dynamic analysis on the same specimen is performed, it may reveal a second feature that has not been detected by the first static analysis. The second feature may have been intentionally hidden by a developer or a provider of the specimen (e.g., a malware author). Such a discrepancy may be determined by controller 106 and/or classifier 105 as a red flag, for example, based on prior statistics collected over a period of time. In such a situation, controller 106 may determine that a further analysis is needed. As a result, a second static analysis may be performed on the specimen in view of the second feature discovered by the first dynamic analysis. The second static analysis may further require a second dynamic analysis to follow.

According to one embodiment, in addition to determining suspicious indicators, static analysis module 102 may further capture non-suspicious indicators, which may be user configurable. The non-suspicious indicators may also be stored in the intelligence store 110. Similarly, in addition to capturing the unexpected behaviors, dynamic analysis module 103 may further record expected behaviors and store the recorded information in the intelligence store 110. For example, if a specimen goes out-of-its-way to look normal during a static analysis, producing non-suspicious indicators, any unexpected behavior detected during a subsequent dynamic analysis may be considered with more weights, since it constitutes discrepancy between the two analyses.

According to one embodiment, in addition to the analysis results 111-112, other information generated by other components (e.g., information stored in the intelligence store 110) may also be presented or available to malware classifier 105. For example, the specimen itself, as well as its environment (e.g., associated email, Web information, and/or related file(s)) may also be presented or available to malware classifier 105.

According to one embodiment, controller 106 may determine, in the middle of a malware detection session based on the information observed, that the current analysis plan was not configured correctly. Controller 106 may decide to abort or abandon the current analysis plan completely and initiate another analysis plan or alternatively, take some correction or recovery actions before continue the current analysis plan. Furthermore, controller 106 may take into account the work load of the malware detection system and may decide to offload the analyses to an offline facility for malware analyses.

According to one embodiment, the number of specimens or the network, email, file work load of the system may also be provided to the malware classifier 105. The type of deployment may also be provided to the malware classifier 105. The controller 106 may determine that specimen has characteristic, identifiers, or behavior that merit sending the specimen outside of the customer's network for additional factory processing, should the customer opt-in to this option.

Note that the configuration of malware detection system 101 is described and shown in FIG. 1A for the purpose of illustration only. More or fewer components or other configurations may be implemented. For example, at least some of the functionalities of classifier 105 may be integrated with controller 106, or vice versa. Each of static analysis module 102, dynamic analysis module 103, and classifier 105 may maintain a separate communications channel (e.g., inter-process call or API as a feedback channel) with controller 106 to communicate with each other. Alternatively, they can communicate with each other via the intelligence store 110 by storing communications information in predetermined storage location(s) of the intelligence store 110 that are shared amongst them. Each of static analysis module 102, dynamic analysis module 103, controller 106, and classifier 105 may be implemented in software, hardware, or a combination thereof. For example, at least some of these components may be implemented as machine-readable code that can be executed by a processor in a memory to carry out the functionalities or operations as described above. Intelligence store 110 may be maintained in a non-volatile storage device such as a hard disk.

Note that an analysis plan may be a formal analysis plan and alternatively, the analysis plan may simply map to some specimen identifiers, one or more analyses, and/or information related to the specimen and the analyses. An analysis plan can be configured or programmed using a variety of programming languages such as extensible markup language (XML) or other scripting languages. The analysis plan may be updatable via a user interface or an API.

FIG. 1B is a block diagram illustrating a malware detection system according to another embodiment of the invention. Referring to FIG. 1B, in addition to those components, such as, controller 106, static analysis module 102, dynamic analysis module 103, malware classifier 105, and intelligence store 110 as shown in FIG. 1A, system 150 further includes an emulation analysis module (also referred to as an emulator or emulation logic) 104 for performing an emulation analysis on a specimen to generate an emulation analysis result 113. Emulation analysis module 104 is communicatively coupled to controller 106, static analysis 102, dynamic analysis module 103, malware classifier 105, and intelligence store 110. In one embodiment, emulation analysis module 104 is configured to emulate operations associated with the processing of a particular specimen in context with an emulated computer application (rather than a “real” application, as may be run in a virtual machine in the dynamic analysis) or in context with an emulated dynamic library. As an optional feature, emulation analysis module 104 may provide the list of functions and other features on which malware checks can be applied in later analyses, and/or information regarding a suitable operating environment to be employed in a virtual machine for dynamic analysis. For example, the emulation analysis module 104 may identify a particular version of an application having a vulnerability targeted the specimen, and the dynamic analysis will then employ that particular version within the virtual environment. This may lead to additional malware indicators and information regarding an attack, which may be stored in the intelligence store 110.

Emulation analysis module 104 is configured to emulate operations of an object and monitor for anomalous behavior. The monitoring may be accomplished by “hooking” certain functions associated with that object (e.g., one or more APIs, etc.), and controlling what data is specifically returned in response to corresponding function calls (e.g., force return of an application version number different than its actual number). After receipt of the returned data, operations by the object are monitored. For instance, the output from the object may be analyzed to determine if a portion of the output matches any of the malware identifiers.

FIG. 2 is a block diagram illustrating an example of a controller according to one embodiment of the invention. Referring to FIG. 2, controller 106 includes, but is not limited to, object capturing logic 201, preliminary filtering logic 202, identifier matching logic 203, and analysis logic 204. Object capturing logic 201 is to fetch or capture a specimen from a content source. The specimen can be Web content, email attachment, or manually submitted content for malware detection. In addition, object capturing logic 201 is to determine or capture certain metadata concerning the circumstances surrounding the specimen. For example, if the specimen is an attachment from an email, certain email attributes, such as, email address(es), SUBJECT field, TO/FROM field, time and date of the email, etc. may be captured. If the specimen is part of Web download, the universal resource locator (URL), domain name, universal resource identifier (URI), type of request chain, protocols, etc. may be captured. In addition, the filename, size, file server from which the file is received, as well as other related files may also be captured. The captured information may be stored in intelligence store 110. Preliminary filtering logic 202 is to perform a preliminary filtering operation on the specimen to determine the type of the specimen (e.g., EXE, PDF, EXCEL, WORD files).

According to one embodiment, identifier matching logic 203 is to match the identifying information of the specimen with a list of identifiers identifying a set of known malware (e.g., black list) and a set of known non-malware (e.g., white list). The list of identifiers may be collected based on prior malware detection and periodically updated from a centralized server in the cloud. If the specimen is identified as one of the matched identifiers in the list, the specimen can be classified right away as either malware or non-malware, without having to perform a further analysis. The identifiers or identifying information may include URLs, observed behaviors, characteristics, features, hash of a malicious object, reputation indicators from third-party reputation service as applied to known malicious sources (e.g., URLs, domain names).

According to one embodiment, analysis logic 204 includes an analysis selector 251, a plan generator 252, and dispatch logic 253. Analysis selector 251 is to select which of the static analysis, dynamic analysis, emulation analysis and classification should be performed. Plan generator 252 is to configure and generate an analysis plan having one or more selected analyses and/or emulation therein. Plan generator 252 is to decide which one or both or how many of a static analysis and dynamic analysis (and emulation analysis, depending on the embodiment) are needed, their sequence order of such analyses to be performed, and other protocol and parameters of these analyses. Plan generator 252 may decide based on a set of rules (not shown), which may be user configurable locally or remotely via a user interface (e.g., command line interface or CLI) and from time to time updated from an external source. Dispatch logic 253 may configure a VM with a set of parameters based on the information provided by object capturing logic 201 and/or preliminary filtering logic 202, based on the customer's specific requirements, or results of prior analysis or analyses. Dispatch logic 253 then dispatches the analysis tasks to any of the analysis modules and classifier in accordance with the analysis plan. All of the information generated from object capturing logic 201, preliminary filtering logic 202, identifier matching logic 203, and Dispatch logic 253 may become part of analysis plan 210 stored in intelligence store 110.

FIG. 3 is a block diagram illustrating a static analysis module according to one embodiment of the invention. Referring to FIG. 3, static analysis module includes metadata extractor 301, profile intelligence extractor 302, deep file intelligence extractor 303, and similarity comparator 304. According to one embodiment, metadata extractor 301 is to extract general metadata from a file. General metadata includes higher level information such as a filename, size, and file structure. Profile intelligence extractor 302 is to extract profile information of the file, including runtime software environment used for processing the file in a virtual environment. Deep file intelligence extractor 303 is to extract a deep object type associated with the file, such as, for example, an embedded object or image within the file. Similarity comparator 304 is to compare the extracted information with prior known information (as may be obtained from, for example, the intelligence store 110) to determine whether the file has been “seen” before. All of the extracted information and comparison information may be stored in a persistent storage such as intelligence store 110, and based on this information, static analysis module 102 produces one or more suspicious indicators if the file is determined to be suspicious.

FIG. 4 is a block diagram illustrating a malware classifier according to one embodiment of the invention. Referring to FIG. 4, classifier 105 includes classification logic 401 and one or more classification models 402. In one embodiment, classification logic 401 examines a static analysis result and/or a dynamic analysis result, in view of all the information stored in intelligence store 110. Classification logic 401 may apply at least some of the suspicious indicators and/or characteristics produced from the static analysis and behavioral information produced from the dynamic analysis, as well as other information from intelligence store 110, to the models 402 to classify the specimen, which may produce one of malware, non-malware, and uncertain indicators.

In one embodiment, intelligence store 110 may include static analysis data store 403 to store any data generated from a static analysis (which may include the static analysis result), dynamic analysis store 404 to store any data generated from a dynamic analysis (which may include the dynamic analysis result), emulation analysis store 406 (which may include the emulation analysis result), and a context store 405 storing any context information, for example, generated from controller 106. Models 402 may be periodically trained and updated, for example, from an external centralized server.

The techniques described above can be applied in a variety of scenarios. For example, in the event that the specimen is a PDF file, static analysis module 102 is configured to determine and generate additional intelligence information in a form of metadata concerning the specimen. The context may include a type of the specimen, a type, version, and/or language of an operating system in which the specimen is intended to be executed, other software components (e.g., a specific version of a PDF reader), and other possible environment settings (e.g., an amount of a memory, a type of a processor, date and time of the operating environment), etc. Based on the context, controller 106 determines or configures an operating environment in which the specimen can be dynamically analyzed by dynamic analysis module 103. In one embodiment, a scheduler (which may be implemented as part of controller 106) provisions and configures a virtual machine (VM) from a pool of VMs based in part on the information obtained from context. In this example, an operating system of the VM may be configured or installed as the same as or similar to the one identified by the context, as well as other software components, virtual memory and processor, etc. Thus, the VM would be configured to be specifically tailored to the targeted operating environment in which the specimen is intended to be processed. As a result, although it can, dynamic analysis module 103 does not have to analyze the specimen in other unrelated or unintended operating environments or using other unrelated or unintended software components, which may significantly improve the efficiency and accuracy of the dynamic analysis.

In addition to weaponized documents, such as a PDF document, the specimen may be a malware type of document, such as a dynamically-link library (DLL). For example, when the specimen in the form of a DLL is received, a static analysis is performed on the content file by static analysis module 102. The static analysis may reveal certain specific processes that are related to the DLL in question. According to one embodiment, when a dynamic analysis is performed, those specific processes, instead of general-purpose processes, may be performed to determine whether the DLL is malicious. As a result, the speed and accuracy of the dynamic analysis can be greatly improved. Further, a static analysis may reveal only certain exported functions existed in the DLL and a dynamic analysis can focus on those existing exported functions without having to test other non-existing functions.

As mentioned above, under certain situations, a dynamic analysis may be performed prior to a static analysis, where the result of the dynamic analysis may be used by the static analysis. For example, if the specimen is a packed DLL file or an executable binary, the static analysis may not be able to fully examine the content based on heuristics. In this situation, a dynamic analysis can unpack the file during processing of the file to reveal other software components (e.g., network stack or other specific libraries). Based on the result of the dynamic analysis, the static analysis can be performed on the unpacked files using related heuristics.

FIG. 5 is a flow diagram illustrating a method for malware detection according to one embodiment of the invention. Method 500 may be performed by processing logic which may include software, hardware, or a combination thereof. For example, method 500 may be performed by system 100 of FIG. 1A. Referring to FIG. 5, at block 501, a controller or control logic determines an analysis plan for analyzing whether a specimen (e.g., content item(s) to be analyzed) should be classified as malware. The analysis plan includes information specifying at least a first analysis and a second analysis to be performed on the specimen. At block 502, a first analysis is performed in accordance with the analysis plan to identify one or more suspicious indicators and one or more characteristics related to and potential useful in the processing of the specimen during the second analysis. At block 503, a second analysis is performed in accordance with the analysis plan on the specimen based on characteristics, if any, identified in the first analysis. The second analysis may include monitoring the specimen in a virtual environment to identify one or more unexpected behaviors having processing or communications anomalies. At block 504, a classifier determines whether the specimen should be classified as malware based on the suspicious indicators and the anomalies of the specimen. At block 505, the analysis plan, the suspicious indicators, characteristics, and anomalies are stored in a persistent storage device.

Note that the specific sequence order of operations as shown in FIG. 5 is described for the purpose of illustration only; other sequence orders of operations may also be performed. For example, after a static analysis has been performed to generate suspicious indicators and characteristics, the classifier may perform a classification based on the suspicious indicators and the characteristics. Based on the classification result, if the controller determines that the result is not conclusive (e.g., uncertain, neither malware nor non-malware), the controller may initiate or configure a further analysis such as a dynamic analysis. Note that in some embodiments, when the result deems to be inconclusive, it simply means an intention or attempt to capture additional suspicious indicators or characteristics in an intelligent manner base on the prior discovery. In one embodiment, an analysis (e.g., static, dynamic, or emulation analysis) may determine that a specimen is malicious, and under certain criteria there may be value in running an additional analysis or analysis steps to capture deeper malicious indicators and/or characteristics. For example, an executable or a PDF file may be declared as malicious based on some analyses. Additional analysis may be performed to capture more stages of the attack. Thus, even though the malicious determination has been made, the controller may decide to continue performing an additional analysis to capture additional threat intelligence about the specimen, which in turn result in additional blocking capability.

In another example, referring back to FIG. 1A or 1B, a specimen is a packed file and captured by controller 106. After a static analysis, static analysis module 102 reveals that the packed file contains no DLL. However, a dynamic analysis performed by dynamic analysis module 103 reveals there are 2 DLLs in the packed file, for example, after unpacking the packed file. Based on the information provided by static and dynamic analysis modules 102-103, controller 106 and/or classifier 105 determine that at least one further analysis is required on the unpacked files. The dynamic analysis may further reveal that the content, when executed, accesses a network, a network stack, and/or a specific library that would not be identified by the static analysis. All of the information generated from static analysis module 102 and dynamic analysis module 103 may be stored in intelligence store and available to all of the components in the system. The discrepancy may be used by the classifier 105 as a factor in classifying the specimen.

In a further example, a first static analysis performed on a specimen determines that the specimen is a packed file. In response, the controller configures a dynamic analysis or emulation performed on the specimen, which may unpack the file. A second static analysis may be performed on the unpacked file. The second static analysis may detect the evasion (also referred to as anti-detection defense or anti-analysis defense) such as virtual machine evasion. Based in part on the detected evasion, a classifier may classify the specimen as malware.

FIG. 6A is a flow diagram illustrating a method for malware detection according to another embodiment of the invention. Method 600 may be performed by systems as shown in FIGS. 1A and 1B, which may be implemented in software, hardware, or a combination thereof. Referring to FIG. 6A, at block 601, a controller or control logic determines an analysis plan for analyzing whether a specimen should be classified as malware. The analysis plan includes information specifying at least one analysis to be performed on the specimen. At block 602, an analysis is performed in accordance with the analysis plan, where the analysis can be a static analysis, a dynamic analysis, or emulation as described above. At block 603, a classifier is invoked to classify the specimen based on a result of the analysis. At block 604, the controller examines the classification result to determine whether the classification is conclusive (e.g., malware or non-malware) or inconclusive (e.g., uncertain or unknown). If the classification is deemed to be conclusive, the current analysis session may end, and a malware identifier or signature may be generated if the specimen is determined to be malware. If the classification is inconclusive, at block 605, the controller may modify the analysis plan to initiate a new analysis or modify a next analysis that has been configured in the plan for further analysis. The operations as shown in FIG. 6A may be iteratively performed until the controller and/or classifier determine that a predetermined criteria (e.g., timing or conclusive result reached) has been satisfied.

FIG. 6B is a flow diagram illustrating a method for malware detection according to another embodiment of the invention. In this example, different sequence orders of analyses of method 650 are shown for the purpose of illustration only. Referring to FIG. 6B, at block 651, a controller determines an analysis plan for analyzing whether a specimen should be classified as malware, where the analysis plan includes one or more analyses. At block 652, a static analysis is performed in accordance with the analysis plan, generating a static analysis result. At block 653, the controller examines the static analysis result to determine whether the result is satisfied. If not, at block 654, an emulation is performed on the specimen in accordance with the analysis plan. At block 655, the controller examines a result of the emulation, and if the result is not satisfied, at block 656, a dynamic analysis is performed on the specimen based on the analysis plan. The operations of FIG. 6B may also be iteratively performed until a predetermined criteria or condition is satisfied.

FIG. 7 is a block diagram illustrating a possible implementation of a malware detection system according to one embodiment of the invention. Malware detection system 700 may be implemented as part of system 101 of FIG. 1A or system 150 of FIG. 1B. System Referring to FIG. 7, system 700 includes a host operating system (OS) (not shown) to manage or control one or more virtual machines (VMs) (also referred to as a sandboxed operating environment or simply a sandbox), where content associated with VMs 752 are stored in storage device 759 in a form of VM disk files 760.

The host OS may host a VM monitor or manager (VMM), also referred to as a hypervisor, for managing or monitoring VMs. VM 752 may be hosted by a guest OS. The host OS and the guest OS may be the same type of operating systems or different types of operating systems (e.g., Windows™, Linux™, Unix™, Mac OS™, iOS™, etc.) or different versions thereof. A VM is a simulation of a machine (abstract or real) that is usually different from the target machine (where it is being simulated on). Virtual machines may be based on specifications of a hypothetical computer or emulate the computer architecture and functions of a real world computer. A virtual machine referred to herein can be any type of virtual machine, such as, for example, hardware emulation, full virtualization, para-virtualization, and operating system-level virtualization virtual machines.

The Host OS further hosts or provides an operating environment to analyzer 751, including static analysis module 102, malware classifier 105, controller 106, and emulation analysis module 104, as described above. According to one embodiment, when a specimen 706 is received for a dynamic analysis (as opposed to a static analysis performed by static analysis module 102), a scheduler 740 is configured to identify and select, or configure a VM, in this example VM 752, from a VM pool 703 that has been configured to closely simulate a target operating environment (e.g., particular version of an OS with particular versions of certain software installed therein) in which specimen 706 is to be analyzed. In one embodiment, based on an analysis result performed by static analysis module 102, a VM such as VM 752 is configured and scheduled by scheduler 740 specifically tailored to an operating environment 710 in which specimen 706 is intended for execution. The scheduler 740 then launches VM 752 in which dynamic analysis module 103 is running within VM 752 and configured to monitor activities and behavior of specimen 706. An emulation analysis may be performed by emulation analysis module 104 as described above. Furthermore, the analysis results generated by static analysis module 102 and/or dynamic analysis module 103 may be stored in corresponding VM disk files 760, for example, as part of intelligence store 110.

FIG. 8 is a block diagram of an illustrative computer network system 800 having a malicious content detection system 850 in accordance with a further illustrative embodiment. In this example, the malicious content detection system is a Web content malware detection system. The malicious content detection system 850 may represent any of the malicious content detection systems described above, such as, for example, detection systems 101 of FIG. 1A, where static analysis module 860 may represent static analysis module 102 and dynamic analysis module 882 may represent dynamic analysis module 103. The malicious content detection system 850 includes controller 106 to coordinate, via an analysis plan, a static analysis and a dynamic analysis in which one analysis may utilize intelligent information produced by another analysis and stored in intelligence store 110. Classifier 105 is to classify whether a particular specimen should be classified as malware based on the static and dynamic analyses. In addition, controller 106 further examines the results of a static analysis and a dynamic analysis to determine whether a further static analysis, dynamic analysis, or both are needed. If so, controller 106 configures or modifies an analysis plan to include at least one additional analysis to be performed, for example, based on the intelligent information provided from the previous analysis, as described above.

The malicious content detection system 850 is illustrated with a server device 810 and a client device 830, each coupled for communication via a communication network 820. In various embodiments, there may be multiple server devices and multiple client devices sending and receiving data to/from each other, and the same device can serve as either a server or a client in separate communication sessions. Although FIG. 8 depicts data transmitted from the server device 810 to the client device 830, either device can transmit and receive data from the other.

Note that throughout this application, network content is utilized as an example of a specimen or specimens for malicious content detection purposes; however, other types of content can also be applied. Network content may include any data transmitted over a network (i.e., network data). Network data may include text, software, images, audio, or other digital data. An example of network content includes web content, or any network data that may be transmitted using a Hypertext Transfer Protocol (HTTP), Hypertext Markup Language (HTML) protocol, or be transmitted in a manner suitable for display on a Web browser software application. Another example of network content includes email messages, which may be transmitted using an email protocol such as Simple Mail Transfer Protocol (SMTP), Post Office Protocol version 3 (POP3), or Internet Message Access Protocol (IMAP4). A further example of network content includes Instant Messages, which may be transmitted using an Instant Messaging protocol such as Session Initiation Protocol (SIP) or Extensible Messaging and Presence Protocol (XMPP). In addition, network content may include any network data that is transferred using other data transfer protocols, such as File Transfer Protocol (FTP).

The malicious network content detection system 850 may monitor exchanges of network content (e.g., Web content) in real-time rather than intercepting and holding the network content until such time as it can determine whether the network content includes malicious network content. The malicious network content detection system 850 may be configured to inspect exchanges of network content over the communication network 820, identify suspicious network content, and analyze the suspicious network content using a virtual machine to detect malicious network content. In this way, the malicious network content detection system 850 may be computationally efficient and scalable as data traffic volume and the number of computing devices communicating over the communication network 820 increases. Therefore, the malicious network content detection system 850 may not become a bottleneck in the computer network system 800.

The communication network 820 may include a public computer network such as the Internet, in which case a firewall 825 may be interposed between the communication network 820 and the client device 830. Alternatively, the communication network may be a private computer network such as a wireless telecommunication network, wide area network, or local area network, or a combination of networks. Though the communication network 820 may include any type of network and be used to communicate different types of data, communications of web data may be discussed below for purposes of example.

The malicious network content detection system 850 is shown as being coupled with the network 820 by a network interface or tap 840 (e.g., a data/packet capturing device). The network tap 840 may include a digital network tap configured to monitor network data and provide a copy of the network data to the malicious network content detection system 850. Network data may comprise signals and data that are transmitted over the communication network 820 including data flows from the server device 810 to the client device 830. In one example, the network tap 840 monitors and copies the network data without an appreciable decline in performance of the server device 810, the client device 830, or the communication network 820. The network tap 840 may copy any portion of the network data, for example, any number of data packets from the network data. In embodiments where the malicious content detection system 850 is implemented as a dedicated appliance or a dedicated computer system, the network tap 840 may include an assembly integrated into the appliance or computer system that includes network ports, network interface card and related logic (not shown) for connecting to the communication network 820 to non-disruptively “tap” traffic thereon and provide a copy of the traffic to the heuristic module 860. In other embodiments, the network tap 840 can be integrated into a firewall, router, switch or other network device (not shown) or can be a standalone component, such as an appropriate commercially available network tap. In virtual environments, a virtual tap (vTAP) can be used to copy traffic from virtual networks.

The network tap 840 may also capture metadata from the network data. The metadata may be associated with the server device 810 and/or the client device 830. For example, the metadata may identify the server device 810 and/or the client device 830. In some embodiments, the server device 810 transmits metadata which is captured by the tap 840. In other embodiments, a heuristic module 860 (described herein) may determine the server device 810 and the client device 830 by analyzing data packets within the network data in order to generate the metadata. The term, “content,” as used herein may be construed to include the intercepted network data and/or the metadata unless the context requires otherwise.

The malicious network content detection system 850 may include a static analysis module 860, a heuristics database (not shown), a scheduler 870, a virtual machine pool 880, a dynamic analysis module 882, an emulator (not shown), and a reporting module 884. In some embodiments, the network tap 840 may be contained within the malicious network content detection system 850. The controller 106 is to coordinate, via an analysis plan, at least one of a static analysis, a dynamic analysis, and an emulation, in which one process may utilize intelligent information produced by another process and stored in intelligence store 110. Classifier 105 is to classify whether a particular specimen should be classified as malware based on the static analysis, dynamic analysis, and/or the emulation. In addition, controller 106 further examines the results of a static analysis and a dynamic analysis to determine whether a further static analysis, dynamic analysis, or both are needed. If so, controller 106 configures a new analysis plan or modifies an existing analysis plan to include at least one additional analysis to be performed, for example, based on the intelligent information provided from the previous analysis, as described above. Controller 106 may monitor or receive a feedback from any of the static analysis module, dynamic analysis module, emulator, and/or the classifier. Based on a result of any of these components, the controller 106 may modify the analysis plan to include a further analysis or alternatively, it may terminate the current analysis if it is determined the result is conclusive.

The static analysis module 860 receives the copy of the network data from the network tap 840 and applies heuristics to the data to determine if the network data might contain suspicious network content. The heuristics applied by the static analysis module 860 may be based on data and/or rules stored in the heuristics database (not shown). The static analysis module 860 may examine the image of the captured content without executing or opening the captured content. For example, the static analysis module 860 may examine the metadata or attributes of the captured content and/or the code image (e.g., a binary image of an executable) to determine whether a certain portion of the captured content matches a predetermined pattern or signature that is associated with a particular type of malicious content. In one example, the static analysis module 860 flags network data as suspicious after applying a heuristic analysis. This detection process is also referred to as static malicious content detection. The suspicious network data may then be provided to the scheduler 870. In some embodiments, the suspicious network data is provided directly to the scheduler 870 with or without buffering or organizing one or more data flows.

When a characteristic of the packet, such as a sequence of characters or keyword, is identified that meets the conditions of a heuristic, a suspicious characteristic of the network content is identified. The identified characteristic may be stored for reference and analysis. In some embodiments, the entire packet may be inspected (e.g., using deep packet inspection techniques) and multiple characteristics may be identified before proceeding to the next step. In some embodiments, the characteristic may be determined as a result of an analysis across multiple packets comprising the network content. A score related to a probability that the suspicious characteristic identified indicates malicious network content is determined.

The static analysis module 860 may also provide a priority level for the packet and/or the features present in the packet. The scheduler 870 may then load and configure a virtual machine from the virtual machine pool 880 in an order related to the priority level, and dispatch the virtual machine to the dynamic analysis module 882 to process the suspicious network content.

The static analysis module 860 may provide the packet containing the suspicious network content to the scheduler 870, along with a list of the features present in the packet and the malicious probability scores associated with each of those features. Alternatively, the static analysis module 860 may provide a pointer to the packet containing the suspicious network content to the scheduler 870 such that the scheduler 870 may access the packet via a memory shared with the static analysis module 860. In another embodiment, the static analysis module 860 may provide identification information regarding the packet to the scheduler 870 such that the scheduler 870, or virtual machine may query the static analysis module 860 for data regarding the packet as needed.

The scheduler 870 may store the received packets, for example, in a queue, and determines an order of processing of the suspicious network content, based on associated priorities assigned to each. The priorities may be based, at least in part, on the results of prior analysis. The scheduler 870 also determines the length of time for processing the suspicious network content based, at least in part, on the results of prior analysis and the waiting queue of network content.

The scheduler 870 may identify an operating environment to be used to process the suspicious network content in a virtual machine, for example, based, at least in part, on the results of the static analysis or other prior analysis. A virtual machine may itself be executable software that is configured with the identified operating environment. The virtual machine may be retrieved from the virtual machine pool 880. Furthermore, the scheduler 870 may identify, for example, an application program required to process the packets, for example, a Web browser, and retrieve a virtual machine equipped with the web browser.

The scheduler 870 may retrieve and configure the virtual machine with features that may include ports that are to receive the network data, select device drivers that are to respond to the network data, and other devices that can respond to the network data. In some embodiments, prior analyses, such as the static analysis, may identified these features. These features may be provided virtually within the virtual environment.

The virtual machine pool 880 may be configured to store one or more virtual machines. The virtual machine pool 880 may include software and/or a storage medium capable of storing software. The virtual machine pool 880 may store any number of distinct virtual machines.

The dynamic analysis module 882 simulates the receipt and/or processing of the network content to analyze the effects (e.g., behaviors) of the network content. There may be multiple dynamic analysis modules 882 to simulate multiple streams of network content. The dynamic analysis module 882 may be configured to monitor the virtual machine for indications that the suspicious network content is in fact malicious network content. Such indications may include unusual network transmissions, unusual changes in performance, and the like. This detection process is referred to as a dynamic malicious content detection.

The dynamic analysis module 882 may flag the suspicious network content as malicious network content according to the observed behavior during processing of the content within the virtual machine. The reporting module 884 may issue alerts indicating the presence of malware, and using pointers and other reference information, identify the packets of the network content containing the malware. This information may include all or an appropriate portion of that stored for the network content in the intelligence store 110. Additionally, the server device 810 may be added to a list of malicious network content providers, and future network transmissions originating from the server device 810 may be blocked from reaching their intended destinations, e.g., by firewall 825.

The computer network system 800 may also include a further communication network 890, which couples the malicious content detection system (MCDS) 850 with one or more other MCDS, of which MCDS 892 and MCDS 894 are shown, and a management system 896, which may be implemented as a Web server having a Web interface. The communication network 890 may, in some embodiments, be coupled for communication with or part of network 820. The management system 896 is responsible for managing the MCDS 850, 892, 894 and providing updates to their operation systems and software programs. Also, the management system 896 may cause malware signatures generated by any of the MCDS 850, 892, 894 to be shared with one or more of the other MCDS 850, 892, 894, for example, on a subscription basis. Moreover, the malicious content detection system as described in the foregoing embodiments may be incorporated into one or more of the MCDS 850, 892, 894, or into all of them, depending on the deployment. Also, the management system 896 itself or another dedicated computer station may incorporate the malicious content detection system in deployments where such detection is to be conducted at a centralized resource.

Further information regarding an embodiment of a malicious content detection system can be had with reference to U.S. Pat. No. 8,171,553, the disclosure of which being incorporated herein by reference in its entirety.

As described above, the detection or analysis performed by the heuristic module 860 may be referred to as static detection or static analysis, which may generate a first score (e.g., a static detection score) according to a first scoring scheme or algorithm. The detection or analysis performed by the analysis engine 882 is referred to as dynamic detection or dynamic analysis, which may generate a second score (e.g., a dynamic detection score) according to a second scoring scheme or algorithm. The first and second scores may be combined, according to a predetermined algorithm, to derive a final score indicating the probability that a malicious content suspect is indeed malicious. Where other analyses are performed, they may result in additional scores may be combined to derive the final score.

Furthermore, detection systems 850 and 892-894 may be deployed in a variety of distribution ways. For example, detection system 850 may be deployed as a detection appliance at a client site to detect any specimen, for example, at a local area network (LAN) of the client. In addition, any of MCDS 892 and MCDS 894 may also be deployed as dedicated data analysis systems. Systems 850 and 892-894 may be configured and managed by a management system 896 over network 890, which may be a LAN, a wide area network (WAN) such as the Internet, or a combination of both. Management system 896 may be implemented as a Web server having a Web interface to allow an administrator of a client (e.g., corporation entity) to log in to manage detection systems 850 and 892-894. For example, an administrator may able to activate or deactivate certain functionalities of malicious content detection systems 850 and 892-894 or alternatively, to distribute software updates such as malicious content definition files (e.g., malicious signatures or patterns) or rules, etc. Furthermore, a user can submit via a Web interface specimen to be analyzed, for example, by dedicated data analysis systems 892-894. As described above, malicious content detection includes static detection and dynamic detection. Such static and dynamic detections can be distributed amongst different systems over a network. For example, static detection may be performed by detection system 850 at a client site, while dynamic detection of the same content can be offloaded to the cloud, for example, by any of detection systems 892-894. Other configurations may exist.

Some portions of the preceding detailed descriptions have been presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the ways used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of operations leading to a desired result. The operations are those requiring physical manipulations of physical quantities.

It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the above discussion, it is appreciated that throughout the description, discussions utilizing terms such as those set forth in the claims below, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.

The techniques shown in the figures can be implemented using code and data stored and executed on one or more electronic devices. Such electronic devices store and communicate (internally and/or with other electronic devices over a network) code and data using computer-readable media, such as non-transitory computer-readable storage media (e.g., magnetic disks; optical disks; random access memory; read only memory; flash memory devices; phase-change memory) and transitory computer-readable transmission media (e.g., electrical, optical, acoustical or other form of propagated signals—such as carrier waves, infrared signals, digital signals).

The processes or methods depicted in the preceding figures may be performed by processing logic that comprises hardware (e.g. circuitry, dedicated logic, etc.), firmware, software (e.g., embodied on a non-transitory computer readable medium), or a combination of both. Although the processes or methods are described above in terms of some sequential operations, it should be appreciated that some of the operations described may be performed in a different order. Moreover, some operations may be performed in parallel rather than sequentially.

In the foregoing specification, embodiments of the invention have been described with reference to specific exemplary embodiments thereof. It will be evident that various modifications may be made thereto without departing from the broader spirit and scope of the invention as set forth in the following claims. The specification and drawings are, accordingly, to be regarded in an illustrative sense rather than a restrictive sense. 

What is claimed is:
 1. A computer implemented method of detecting malware in a specimen of computer content or network traffic, the method comprising: responsive to receiving a specimen, determining, by a controller, an analysis plan for analyzing whether the received specimen should be classified as malware, the analysis plan identifying at least a first analysis and a second analysis to be performed with respect to the specimen, each of the first analysis and the second analysis identified in the analysis plan including at least one of a static analysis and a dynamic analysis; performing the first analysis in accordance with the analysis plan to identify one or more suspicious indicators associated with malware and one or more characteristics related to processing of the specimen; performing the second analysis in accordance with the analysis plan, including processing of the specimen in a virtual environment including with one or more monitors to identify one or more unexpected behaviors each having one or more processing or communications anomalies; determining by a classifier whether the specimen should be classified as malicious based on the one or more identified suspicious indicators and the one or more identified anomalies; and storing the analysis plan, the one or more identified indicators, the one or more characteristics, and the one or more identified anomalies in a persistent memory.
 2. The method of claim 1, wherein the first analysis comprises a static analysis, and wherein performing the second analysis is based in part on at least one of the identified suspicious indicators and characteristics identified in the first analysis.
 3. The method of claim 1, wherein the first analysis comprises a dynamic analysis, and wherein performing the second analysis is based in part on at least one of the anomalies identified in the first analysis.
 4. The method of claim 1, further comprising: receiving by the control logic, over a first feedback path, the identified suspicious indicators and characteristics, which include results of the first analysis; modifying the analysis plan in response to the results of the first analysis; and storing the modified analysis plan in the persistent memory.
 5. The method of claim 4, wherein the first analysis comprises a static analysis and the second analysis comprises a dynamic analysis, wherein modifying the analysis plan comprises adding a third analysis comprising a static analysis; and wherein the method further comprises performing the third analysis after the first and second analyses are performed.
 6. The method of claim 5, wherein the first analysis comprises a static analysis; the second analysis comprises a dynamic analysis; and wherein modifying to the analysis plan comprises adding a third analysis comprising processing the specimen in an emulator, and a fourth analysis comprising a static analysis; and wherein the method further comprises performing, after the first analysis and before the second analyses are performed, the third analysis to unpack an object in the specimen, and performing the fourth analysis on at least the unpacked object of the specimen.
 7. The method of claim 1, further comprising: receiving, by the control logic, the identified suspicious indicators and characteristics which comprise results of the first analysis; determining whether a third analysis should be performed; and if so determined, modifying the analysis plan to reflect the third analysis and initiating the third analysis in accordance with the modified analysis plan.
 8. The method of claim 1, further comprising: receiving, the control logic, the identified suspicious indicators and characteristics which comprise results of the first analysis; storing a plurality of specimens to be analyzed; associating a priority to each of the specimens based on the number of specimens to be analyzed; setting an order of processing of the specimens to be analyzed based on their respective priority; and modifying the priority and order of processing in response to the first analysis.
 9. The method of claim 1, further comprising: receiving, the control logic, the identified suspicious indicators and characteristics which comprise results of the first analysis; storing a plurality of specimens to be analyzed, associating a priority to each of the specimens; setting a time for analysis in at least one of the first and second analyzes based on their respective priorities; and storing the time in the analysis plan.
 10. The method of claim 1, wherein the first analysis comprises a static analysis, and wherein the method further comprising selecting a runtime environment for the virtual environment based on the static analysis, wherein selecting the runtime environment comprises determining an operating system and version thereof, and an application and version thereof, to be processed with the specimen in the virtual environment, and modifying the analysis plan to reflect the selected runtime environment.
 11. The method of claim 10, further comprising the control logic receiving the identified suspicious indicators and characteristics which comprise results of the first analysis; and wherein selecting the runtime environment for the virtual environment comprises selecting an initial state from which the application will be run based on the results of the first analysis.
 12. The method of claim 1, further comprising dispatching the specimen for both static and dynamic analysis to be run during a selected period of time by respective static analysis logic and dynamic analysis logic.
 13. The method of claim 1, wherein the static analysis comprises comparing the specimen with an identifier of known malware, and further comprising classifying the specimen as malicious based on the comparison.
 14. The method of claim 1, further comprising: generating a malware identifier based on the one or more suspicious indicators, the one or more identified anomalies, and the analysis plan; and using the malware identifier to identify one or more other specimens as malicious.
 15. The method of claim 1, further comprising accessing a database of intelligence regarding detection of malware in other specimens and determining the analysis plan based on the intelligence stored therein.
 16. The method of claim 15, wherein the virtual environment comprises a runtime environment, and the method further comprising tuning the runtime environment based on the intelligence stored in the database.
 17. The method of claim 1, further comprising: capturing objects comprising the specimen; pre-filtering the captured objects to identify an object type, an object type comprising an executable, the characteristic of the specimen comprising the object type; and performing the first analysis after the pre-filtering step.
 18. The method of claim 1, further comprising performing the static analysis by a static analyzer, including: extracting from the specimen an object type comprising determining, for the object type, additional metadata useful for processing the specimen in the virtual environment, the characteristic of the specimen comprising the additional metadata; and extracting from the specimen a profile intelligence comprising a runtime software environment used for processing the specimen in the virtual environment, the characteristic of the specimen comprising the profile intelligence.
 19. The method of claim 1, wherein determining by the classifier whether the specimen should be classified as malicious comprises comparing the suspicious indicators from the static analysis conducted on the specimen, if any, and the identified anomalies from any dynamic analysis conducted on the specimen, if any, with a model of indicators and anomalies associated with known malware and known non-malware computer content and traffic.
 20. The method of claim 1, wherein the analysis plan further specifies one of reputation checking and malware source blacklist checking.
 21. A system of detecting malware in a specimen of computer content or network traffic, the system comprising: control logic to receive a specimen and determine an analysis plan for analyzing whether the specimen should be classified as malware, the analysis plan identifying at least first and second analyses to be performed with respect to the specimen, each of the first and second analyses identified in the analysis plan including one or both of a static analysis and a dynamic analysis; a first analysis module to perform the first analysis in accordance with the analysis plan to identify one or more suspicious indicators associated with malware and one or more characteristics related to processing of the specimen; a second analysis module to perform the second analysis in accordance with the analysis plan, including processing of the specimen in a virtual environment equipped with one or more monitors to identify one or more unexpected behaviors having one or more processing or communications anomalies; a classifier to determine whether the specimen should be classified as malicious based on the one or more identified suspicious indicators and the one or more identified anomalies; and a persistent storage device to store the analysis plan, the one or more identified indicators, the one or more characteristics, and the one or more identified anomalies.
 22. The system of claim 21, wherein the first analysis comprises a static analysis, and wherein performing the second analysis is based in part on at least one of the identified suspicious indicators and characteristics identified in the first analysis.
 23. The system of claim 21, wherein the first analysis comprises a dynamic analysis, and wherein performing the second analysis is based in part on at least one of the anomalies identified in the first analysis.
 24. A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform a method of detecting malware in a specimen of computer content or network traffic, the method comprising: receiving a specimen and determining, by control logic, an analysis plan for analyzing whether the specimen should be classified as malware, the analysis plan identifying at least first and second analyses to be performed with respect to the specimen, each of the first and second analyses identified in the analysis plan including one or both of a static analysis and a dynamic analysis; performing the first analysis in accordance with the analysis plan to identify one or more suspicious indicators associated with malware and one or more characteristics related to processing of the specimen; performing the second analysis in accordance with the analysis plan, including processing of the specimen in a virtual environment equipped with one or more monitors to identify one or more unexpected behaviors having one or more processing or communications anomalies; determining by a classifier whether the specimen should be classified as malicious based on the one or more identified suspicious indicators and the one or more identified anomalies; and storing the analysis plan, the one or more identified indicators, the one or more characteristics, and the one or more identified anomalies in a persistent memory.
 25. The non-transitory machine-readable medium of claim 24, wherein the first analysis comprises a static analysis, and wherein performing the second analysis is based in part on at least one of the identified suspicious indicators and characteristics identified in the first analysis.
 26. The non-transitory machine-readable medium of claim 24, wherein the first analysis comprises a dynamic analysis, and wherein performing the second analysis is based in part on at least one of the anomalies identified in the first analysis.
 27. A computer implemented method of detecting malware in a specimen of computer content or network traffic, the method comprising: responsive to receiving a specimen, determining, by a controller, an analysis plan for analyzing whether the received specimen should be classified as malware, the analysis plan identifying at least a first analysis to be performed with respect to the specimen, the first analysis being one of a static analysis and a dynamic analysis; performing the first analysis in accordance with the analysis plan to identify at least one of one or more suspicious indicators associated with malware, one or more characteristics related to processing of the specimen, and one or more unexpected behaviors each having one or more processing or communications anomalies; determining by a classifier whether the specimen should be classified as malicious based on the one or more identified suspicious indicators and the one or more identified anomalies; and modifying, by the controller, the analysis plan, to include a second analysis to be performed based on at least one of the suspicious indicators, characteristics of the specimen, and anomalies produced by the first analysis, and a result of the classifier.
 28. The method of claim 27, wherein the first analysis is a static analysis and the second analysis is a dynamic analysis.
 29. The method of claim 27, further comprising storing in a persistent memory the specimen, the suspicious indicators, characteristics of the specimen, the anomalies, and the result of classification performed by the classifier.
 30. A computer implemented method of detecting malware in a specimen of computer content or network traffic, the method comprising: responsive to receiving a specimen, determining an analysis plan for analyzing whether the received specimen should be classified as malware, the analysis plan identifying at least a first analysis to be performed with respect to the specimen, the first analysis being one of a static analysis, a dynamic analysis, and emulation; dispatching the analysis plan to an analysis module to perform the first analysis in accordance with the analysis plan; receiving a first analysis result from the analysis module, the first analysis result having at least one of one or more suspicious indicators associated with malware, one or more characteristics related to processing of the specimen, and one or more unexpected behaviors each having one or more processing or communications anomalies, which is identified by the first analysis; determining whether the specimen should be classified as malicious based on the one or more identified suspicious indicators and the one or more identified anomalies; and in response to determining that the determination is inconclusive, modifying the analysis plan to include a second analysis to be performed based on at least one of the suspicious indicators, characteristics of the specimen, and anomalies produced by the first analysis.
 31. The method of claim 30, wherein determining whether the specimen should be classified as malicious comprises: dispatching the analysis plan to a classifier to classify the specimen based on the one or more identified suspicious indicators and the one or more identified anomalies; and receiving a classification indicator from the classifier indicating whether the specimen is malware, non-malware, or uncertain.
 32. The method of claim 30, further comprising dispatching the modified analysis plan to a second analysis module to perform the second analysis in accordance with the modified analysis plan.
 33. The method of claim 32, wherein the first analysis is a static analysis and the second analysis is a dynamic analysis.
 34. The method of claim 30, further comprising storing the analysis plan and the first analysis result in a persistent storage device. 